Lets look at snowcover for YELL, in October. The understory phenocam images for that month looks interesting, with snowfall and periods of snow coming and going thruout the month:

_A screenshot of the YELL understory phenocam images for October 2019_

A screenshot of the YELL understory phenocam images for October 2019 

site = "YELL"

if(!file.exists("octoberSnow.RDS")){
# Make a sequence of dates that we're interested in, in Oct
# Is this needlessly complicated? Yes. 
october=1:lubridate::days_in_month(10) %>%
  stringr::str_pad(string = ., width = 2, side = "left", pad = "0") %>%
  paste0("2019-10-", .)

# generate a list of phenocam urls
phenoUrls=lapply(october, function(date) ytBangerz::getPhenoUrls(site = site, date = date)) %>%
  unlist %>% 
  na.omit() %>%
  as.character()



snowPortion=lapply(phenoUrls, ytBangerz::detectSnow) %>% unlist()

# we had to drop some urls for not having noon urls. what did we get
availableDates=stringr::str_extract(string = phenoUrls, pattern = "[0-9]{4}_[0-9]{2}_[0-9]{2}") %>% 
  gsub(pattern = "_", replacement = "-")

octoberSnow=data.frame("Date"=as.Date(availableDates),"Portion"=snowPortion*100)
saveRDS(octoberSnow, file = "octoberSnow.RDS")
}else{
  octoberSnow=readRDS("octoberSnow.RDS")
}

knitr::kable(octoberSnow)
Date Portion
2019-10-01 36.024425
2019-10-02 44.196007
2019-10-03 8.816110
2019-10-04 6.601509
2019-10-05 46.060909
2019-10-06 0.000000
2019-10-07 0.000000
2019-10-11 50.360844
2019-10-12 44.129420
2019-10-13 38.639117
2019-10-14 34.569616
2019-10-15 13.368961
2019-10-16 6.321921
2019-10-17 4.861587
2019-10-18 6.973022
2019-10-19 4.825198
2019-10-20 73.535856
2019-10-21 70.996514
2019-10-22 49.130944
2019-10-23 73.043458
2019-10-24 68.130049
2019-10-25 32.481805
2019-10-26 71.871094
2019-10-27 37.558585
2019-10-28 48.191015
2019-10-29 42.374733
2019-10-30 39.073217
2019-10-31 59.709457 
savedImage=paste0(site, " 2019.png")
if(!file.exists(savedImage)){
p=ggplot2::ggplot(data=octoberSnow, ggplot2::aes(x=Date, y=Portion))+
  ggplot2::geom_path(na.rm = T, color="#0e1675")+
  ggplot2::geom_area(fill="#6449fc", alpha=0.4)+
  ggplot2::scale_x_date(date_breaks = "1 month", date_labels = "%B")+
  ggplot2::scale_y_continuous(limits = c(0, 100))+
  ggplot2::ggtitle(label = paste0(site,  " Snow Coverage"),
                   subtitle = "Percent of understory phenocamera image classified as snow")+
  ggplot2::theme_bw()+ggplot2::theme(axis.text.x = ggplot2::element_text(angle=-45, vjust = -.7))

p=ggplot2::ggplot(data=octoberSnow, ggplot2::aes(x=Date, y=Portion))+
  #ggplot2::geom_step()+
  ggplot2::geom_col(width = 1, alpha=0.8, 
                    fill="#0e1675")+
  
  # ggplot2::geom_col(na.rm = T, color="#0e1675", fill="#0e1675")+
  # ggplot2::geom_area(fill="#6449fc", alpha=0.4)+
  ggplot2::scale_x_date(date_breaks = "1 week", date_labels = "%D")+
  ggplot2::scale_y_continuous(limits = c(0, 100))+
  ggplot2::ggtitle(label = paste0(site,  "- October 2019 Snow Coverage"),
                   subtitle = "Percent of understory phenocamera image classified as snow")+
  ggplot2::theme_bw()+ggplot2::theme(axis.text.x = ggplot2::element_text(angle=-45, vjust = -.7))


ggplot2::ggsave(filename = savedImage, plot = p, device = "png", width = 6, height = 4, units = "in", dpi = 180)
}
_A screenshot of the YELL understory phenocam images for October 2019_

A screenshot of the YELL understory phenocam images for October 2019